{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c2dfb095",
   "metadata": {},
   "source": [
    "# Explainability for Deep Learning Forecasting Models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23d25d05",
   "metadata": {},
   "source": [
    "In this detailed tutorial, we discover how to explain forecasts made with models from *neuralforecast*. \n",
    "\n",
    "Note that the functionality is currently in beta. It can only be applied on univariate models, but support for multivariate models is coming soon."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "598f35c8",
   "metadata": {},
   "source": [
    "\n",
    "## Prerequisites"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3660ea43",
   "metadata": {},
   "source": [
    "- We assume you have *neuralforecast* already installed. \n",
    "- Explanations are obtained with [Captum](https://captum.ai/): an open-source library for model interpretability in PyTorch. Make sure to install the package with `pip install captum` to use the features demonstrated below. \n",
    "- You can optionally install [SHAP](https://shap.readthedocs.io/en/latest/) to access their visualizations capabilities. This can be done with `pip install shap`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f20cc4b1",
   "metadata": {},
   "source": [
    "## Load libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8de329b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "\n",
    "from neuralforecast.core import NeuralForecast\n",
    "from neuralforecast.models import NHITS\n",
    "from neuralforecast.losses.pytorch import MQLoss\n",
    "from neuralforecast.utils import AirPassengersPanel, AirPassengersStatic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7d658efd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set random seeds for reproducibility\n",
    "np.random.seed(42);\n",
    "torch.manual_seed(42);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bae3a9d",
   "metadata": {},
   "source": [
    "## Load the data\n",
    "\n",
    "We demonstrate the explainability capabilities with the AirPassengers dataset. This dataset has:\n",
    "- 2 unique series\n",
    "- a future exogenous variable (`trend`)\n",
    "- a historical exogenous variable (`y_lag[12]`)\n",
    "- static exogenous variable (`Airline1`)\n",
    "\n",
    "That way, we see that we can handle attributions for all types of exogenous features. For more information on the types of exogenous features, read [this tutorial](https://nixtlaverse.nixtla.io/neuralforecast/docs/capabilities/exogenous_variables.html). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fb656fdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_train_df = AirPassengersPanel[AirPassengersPanel['ds'] < AirPassengersPanel['ds'].values[-12]].reset_index(drop=True)\n",
    "Y_test_df = AirPassengersPanel[AirPassengersPanel['ds'] >= AirPassengersPanel['ds'].values[-12]].reset_index(drop=True)\n",
    "futr_df = Y_test_df.drop(columns=[\"y\", \"y_[lag12]\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7337e61d",
   "metadata": {},
   "source": [
    "## Basic usage\n",
    "### Train a model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27a4840a",
   "metadata": {},
   "source": [
    "Before explaining forecasts, we need to train a forecasting model. Here, we use the NHITS model, but you can use any univariate model. For now, we don't support multivariate models just yet, this feature will be implemented soon."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2f22877c",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "\n",
    "models = [\n",
    "    NHITS(\n",
    "        h=12,\n",
    "        input_size=24,\n",
    "        hist_exog_list=[\"y_[lag12]\"],\n",
    "        futr_exog_list=[\"trend\"],\n",
    "        stat_exog_list=['airline1'],\n",
    "        max_steps=20,\n",
    "        scaler_type=\"robust\",\n",
    "    ),\n",
    "]\n",
    "\n",
    "nf = NeuralForecast(\n",
    "    models=models, \n",
    "    freq=\"ME\", \n",
    ")\n",
    "\n",
    "nf.fit(\n",
    "    df=Y_train_df,\n",
    "    static_df=AirPassengersStatic\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "704499e5",
   "metadata": {},
   "source": [
    "### Get features attributions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fa3bcfd",
   "metadata": {},
   "source": [
    "Once the model is trained, we can get feature attributions using the `nf.explain` method.\n",
    "\n",
    "This method takes the following parameters:\n",
    "- `horizons`: List of horizons to explain. If None, all horizons are explained. Defaults to None.\n",
    "- `outputs`: List of outputs to explain for models with multiple outputs. Defaults to [0] (first output). This is useful when we have models trained with a probabilistic loss. We will explore that later in the tutorial.\n",
    "- `explainer`: Name of the explainer to use. Options are 'IntegratedGradients', 'ShapleyValueSampling', 'Lime', 'KernelShap', 'InputXGradient'. Defaults to 'IntegratedGradients'.\n",
    "- `df` (pandas, polars or spark DataFrame): DataFrame with columns [`unique_id`, `ds`, `y`] and exogenous variables. If a DataFrame is passed, it is used to generate forecasts. Defaults to None.\n",
    "- `static_df` (pandas, polars or spark DataFrame): DataFrame with columns [`unique_id`] and static exogenous. Defaults to None. Only use it if you trained your model with static exogenous features.\n",
    "- `futr_df` (pandas, polars or spark DataFrame): DataFrame with [`unique_id`, `ds`] columns and `df`'s future exogenous. Defaults to None. Only use it if you trained your model with future exogenous features.\n",
    "- `verbose`: Print warnings. Defaults to True.\n",
    "- `engine`: Distributed engine for inference. Only used if df is a spark dataframe or if fit was called on a spark dataframe.\n",
    "- `level`: Confidence levels between 0 and 100. Defaults to None.\n",
    "- `quantiles`: Alternative to level, target quantiles to predict. Defaults to None.\n",
    "- `data_kwargs`: Extra arguments to be passed to the dataset within each model.\n",
    "\n",
    "Note that parameters from `df` and onwards act exactly the same way as in the `nf.predict()` method. \n",
    "\n",
    "In this case, let's explain each horizon step, so we keep `horizons=None`. Since our model used a point loss, there is only one output, so we also keep the default value `outputs=[0]`. Finally, we choose the \"IntegratedGradients\" explainer, as it is one of the fastest method for interpretability in deep learning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cfb9bfe0",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "\n",
    "preds_df, explanations = nf.explain(\n",
    "    static_df=AirPassengersStatic,\n",
    "    futr_df=futr_df, \n",
    "    explainer=\"IntegratedGradients\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73ea47be",
   "metadata": {},
   "source": [
    "We can see that `nf.explain()` returns two values:\n",
    "\n",
    "1. A dataframe with the forecasts from the fitted models\n",
    "2. A dictionary with the feature attributions for each model\n",
    "\n",
    "Thus, you can access the attribution score of each features used for training the NHITS model by accessing `explanations[\"NHITS\"]`. Note that if you used an alias when initializing the model, then the key is the value of the alias."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "086b83b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['insample', 'futr_exog', 'hist_exog', 'stat_exog', 'baseline_predictions'])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "explanations[\"NHITS\"].keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4774683",
   "metadata": {},
   "source": [
    "From above, we can see that we have stored the attributions for each feature type as well as the baseline predictions.\n",
    "- `insample` contains the attributions for past lags and availability mask\n",
    "- `futr_exog` contains the attributions for future exogenous features\n",
    "- `hist_exog` contains the attributions for historical exogenous features\n",
    "- `stat_exog` contains the attributions for static exogenous features\n",
    "- `baseline_predictions` contains the baseline prediction of the model if none of the features above were available. Note that if the selected explainer does not have the additivity property, the value will be set to None.\n",
    "\n",
    "We will touch upon the additivity property in a later section. For now, just know that `IntegratedGradients` has the additive property, meaning that taking the sum of baseline predictions and feature attributions results in the final forecast made by the model.\n",
    "\n",
    "Now, because we are using Captum, we work directly with tensors, keeping the entire process fast, efficient, and allowing us to leverage GPUs when available. As such, the attributions are also stored as tensors as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9eeb44c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of insample: torch.Size([2, 12, 1, 1, 24, 2])\n",
      "Shape of futr_exog: torch.Size([2, 12, 1, 1, 36, 1])\n",
      "Shape of hist_exog: torch.Size([2, 12, 1, 1, 24, 1])\n",
      "Shape of stat_exog: torch.Size([2, 12, 1, 1, 1])\n",
      "Shape of baseline_predictions: torch.Size([2, 12, 1, 1])\n"
     ]
    }
   ],
   "source": [
    "for key in list(explanations[\"NHITS\"].keys()):\n",
    "    print(f\"Shape of {key}: {explanations['NHITS'][key].shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b92db245",
   "metadata": {},
   "source": [
    "For each element above, the shape is defined as:\n",
    "\n",
    "- `insample`: [batch_size, horizon, n_series, n_output, input_size, 2 (y attribution, mask attribution)]\n",
    "- `futr_exog`: [batch_size, horizon, n_series, n_output, input_size+horizon, n_futr_features]\n",
    "- `hist_exog`: [batch_size, horizon, n_series, n_output, input_size, n_hist_features]\n",
    "- `stat_exog`: [batch_size, horizon, n_series, n_output, n_static_features]\n",
    "- `baseline_predictions`: [batch_size, horizon, n_series, n_output]\n",
    "\n",
    "Here, `batch_size` is 2 for all, because we are explaining two different series. `n_series` however is 1 because NHITS is a univariate model. Also note that for `insample`, the last shape is always 2, because we score the attribution of the values of the past lags and their availability.\n",
    "\n",
    "At this point, we have all the information needed to analyze the attribution scores and make visualizations."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "422a14f3",
   "metadata": {},
   "source": [
    "### Plotting feature attributions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c24c744",
   "metadata": {},
   "source": [
    "You can now use any method you want to plot feature attributions. You can make plots manually using any visualization library like `matplotlib` or `seaborn`, but `shap` has dedicated plots for explainability, so let's see how we can use them.\n",
    "\n",
    "Basically, with the information we have, we can easily create a `shap.Explanation` object that can then be used to create different plots from the `shap` package.\n",
    "\n",
    "Specifically, a `shap.Explanation` object needs:\n",
    "- `values`: the attribution scores\n",
    "- `base_values`: the baseline predictions of the model\n",
    "- `feature_names`: a list to display nice feature names\n",
    "\n",
    "Here, let's create a waterfall plot to visualize the attributions of each features, for the first series (Airline1), and for the first step in the horizon. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "db67e827",
   "metadata": {},
   "outputs": [],
   "source": [
    "import shap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "38879d40",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x650 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "batch_idx = 0    # Attributions for the first series (Airline1)\n",
    "horizon_idx = 0  # Attributions for the first horizon step\n",
    "output_idx = 0\n",
    "\n",
    "attributions = []\n",
    "feature_names = []\n",
    "\n",
    "# Insample attributions\n",
    "y_attr = explanations[\"NHITS\"][\"insample\"][batch_idx, horizon_idx, 0, output_idx, :, 0]\n",
    "mask_attr = explanations[\"NHITS\"][\"insample\"][batch_idx, horizon_idx, 0, output_idx, :, 1]\n",
    "combined_insample = (y_attr + mask_attr).cpu().numpy()\n",
    "for i, attr in enumerate(combined_insample):\n",
    "    attributions.append(attr)\n",
    "    feature_names.append(f\"y_lag{i+1}\")\n",
    "\n",
    "# hist_exog attributions\n",
    "hist_attr = explanations[\"NHITS\"][\"hist_exog\"][batch_idx, horizon_idx, 0, output_idx]\n",
    "hist_attr = hist_attr.cpu().numpy()\n",
    "for t in range(hist_attr.shape[0]):\n",
    "    attributions.append(hist_attr[t, 0])\n",
    "    feature_names.append(f\"y_lag12_t{t+1}\")\n",
    "\n",
    "# futr_exog attributions\n",
    "futr_attr = explanations[\"NHITS\"][\"futr_exog\"][batch_idx, horizon_idx, 0, output_idx]\n",
    "futr_attr = futr_attr.cpu().numpy()\n",
    "for t in range(futr_attr.shape[0]):\n",
    "    attributions.append(futr_attr[t, 0])\n",
    "    if t < 24:\n",
    "        feature_names.append(f\"trend_hist_t{t+1}\") # Known values in the past\n",
    "    else:\n",
    "        feature_names.append(f\"trend_futr_h{t-23}\") # Knwon values in the future\n",
    "\n",
    "# stat_exog attributions\n",
    "stat_attr = explanations[\"NHITS\"][\"stat_exog\"][batch_idx, horizon_idx, 0, output_idx]\n",
    "attributions.append(float(stat_attr.cpu()))\n",
    "feature_names.append(\"airline1\")\n",
    "\n",
    "shap_values = np.array(attributions)\n",
    "\n",
    "# baseline_predictions\n",
    "baseline = float(explanations[\"NHITS\"][\"baseline_predictions\"][batch_idx, horizon_idx, 0, output_idx].cpu())\n",
    "\n",
    "# Create SHAP Explanation\n",
    "shap_explanation = shap.Explanation(\n",
    "    values=shap_values,\n",
    "    base_values=baseline,\n",
    "    feature_names=feature_names\n",
    ")\n",
    "\n",
    "shap.plots.waterfall(shap_explanation)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "644a36fa",
   "metadata": {},
   "source": [
    "As you can see, we now have a nice waterfall plot showing the baseline prediction, E[f(X)] = 396.163, and how each features contributes to the final forecast f(x) = 418.631.\n",
    "\n",
    "Of course, we can do a wide variery of different plots from `shap`. For example, we can do a simple bar plot as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "13c825e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x650 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.plots.bar(shap_explanation)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce8cc385",
   "metadata": {},
   "source": [
    "In both figures above, we have the breakdown of each feature at each timestep. This can make the plots crowded or it can be a level of granularity that is not necessary for analysis. So, you can also decide to combine all time steps together for a cleaner plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e45a59e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "batch_idx = 0\n",
    "horizon_idx = 0\n",
    "output_idx = 0\n",
    "\n",
    "baseline = float(explanations[\"NHITS\"][\"baseline_predictions\"][batch_idx, horizon_idx, output_idx, output_idx].cpu())\n",
    "insample_sum = float(explanations[\"NHITS\"][\"insample\"][batch_idx, horizon_idx, output_idx, output_idx, :, :].sum().cpu())\n",
    "\n",
    "futr_exog_sum = 0\n",
    "futr_exog_sum = float(explanations[\"NHITS\"][\"futr_exog\"][batch_idx, horizon_idx, output_idx, output_idx, :, :].sum().cpu())\n",
    "\n",
    "hist_exog_sum = 0\n",
    "hist_exog_sum = float(explanations[\"NHITS\"][\"hist_exog\"][batch_idx, horizon_idx, output_idx, output_idx, :, :].sum().cpu())\n",
    "\n",
    "stat_exog_sum = 0\n",
    "stat_exog_sum = float(explanations[\"NHITS\"][\"stat_exog\"][batch_idx, horizon_idx, output_idx, output_idx, :].sum().cpu())\n",
    "\n",
    "feature_names = []\n",
    "shap_values = []\n",
    "\n",
    "if insample_sum != 0:\n",
    "    feature_names.append(\"Historical Y (all lags)\")\n",
    "    shap_values.append(insample_sum)\n",
    "\n",
    "if hist_exog_sum != 0:\n",
    "    feature_names.append(\"Historical Exog (y_lag12)\")\n",
    "    shap_values.append(hist_exog_sum)\n",
    "\n",
    "if futr_exog_sum != 0:\n",
    "    feature_names.append(\"Future Exog (trend)\")\n",
    "    shap_values.append(futr_exog_sum)\n",
    "\n",
    "if stat_exog_sum != 0:\n",
    "    feature_names.append(\"Static (airline1)\")\n",
    "    shap_values.append(stat_exog_sum)\n",
    "\n",
    "shap_values = np.array(shap_values)\n",
    "\n",
    "# Create SHAP Explanation\n",
    "shap_explanation = shap.Explanation(\n",
    "    values=shap_values,\n",
    "    base_values=baseline,\n",
    "    feature_names=feature_names\n",
    ")\n",
    "\n",
    "shap.plots.waterfall(shap_explanation)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e63c9f1a",
   "metadata": {},
   "source": [
    "As you can see from the plot above, we have combined all inputs of each type of feature into a single category, so we can see how each overall feature contributes to the final forecast."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db601b36",
   "metadata": {},
   "source": [
    "### Verifying additivity\n",
    "\n",
    "As mentioned above, \"IntegratedGradients\" has the additive property, meaning that when we sum the baseline predictions with the total attribution scores of each features, we get the final forecasts made by the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "299e7399",
   "metadata": {},
   "outputs": [],
   "source": [
    "attribution_predictions = []\n",
    "\n",
    "# Process each series\n",
    "for batch_idx in range(2):  # 2 series\n",
    "    # Process each horizon for this series\n",
    "    for horizon_idx in range(12):  # horizon = 12\n",
    "        # Get baseline\n",
    "        baseline = float(explanations[\"NHITS\"][\"baseline_predictions\"][batch_idx, horizon_idx, 0, 0].cpu())\n",
    "        \n",
    "        # Sum all attribution components\n",
    "        total_attr = 0\n",
    "        \n",
    "        # Insample (y + mask)\n",
    "        insample_attr = explanations[\"NHITS\"][\"insample\"][batch_idx, horizon_idx, 0, 0, :, :].sum()\n",
    "        total_attr += float(insample_attr.cpu())\n",
    "        \n",
    "        # Historical exogenous\n",
    "        if explanations[\"NHITS\"][\"hist_exog\"] is not None:\n",
    "            hist_attr = explanations[\"NHITS\"][\"hist_exog\"][batch_idx, horizon_idx, 0, 0, :, :].sum()\n",
    "            total_attr += float(hist_attr.cpu())\n",
    "        \n",
    "        # Future exogenous\n",
    "        if explanations[\"NHITS\"][\"futr_exog\"] is not None:\n",
    "            futr_attr = explanations[\"NHITS\"][\"futr_exog\"][batch_idx, horizon_idx, 0, 0, :, :].sum()\n",
    "            total_attr += float(futr_attr.cpu())\n",
    "        \n",
    "        # Static exogenous\n",
    "        if explanations[\"NHITS\"][\"stat_exog\"] is not None:\n",
    "            stat_attr = explanations[\"NHITS\"][\"stat_exog\"][batch_idx, horizon_idx, 0, 0, :].sum()\n",
    "            total_attr += float(stat_attr.cpu())\n",
    "        \n",
    "        # Compute final prediction from attributions\n",
    "        pred_from_attr = baseline + total_attr\n",
    "        attribution_predictions.append(pred_from_attr)\n",
    "\n",
    "# Add as new column to preds_df\n",
    "preds_df['NHITS_attribution'] = attribution_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "95c1dc74",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.testing.assert_allclose(\n",
    "    preds_df['NHITS'].values,\n",
    "    preds_df['NHITS_attribution'].values,\n",
    "    rtol=1e-3,\n",
    "    err_msg=\"Attribution predictions do not match model predictions\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95ec9082",
   "metadata": {},
   "source": [
    "From the code above, we can see that reconstructed forecasts from the baseline predictions and attributions are within 0.1% of the original forecasts, so additivity is verified."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b9d3433",
   "metadata": {},
   "source": [
    "## Advanced concepts\n",
    "### Choosing an explainer\n",
    "\n",
    "In this section, we outline the different explainers supported in *neuralforecast*. Different algorithms will produce different attribution scores, and so we must choose which applies best to our scenario."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f75d5023",
   "metadata": {},
   "source": [
    "| Explainer | Local/Global | Additivity Property | Speed |\n",
    "|-----------|--------------|-------------------|-------|\n",
    "| Integrated Gradients | Local | Yes | Fast |\n",
    "| Shapley Value Sampling | Local | Yes | Very slow |\n",
    "| Input X Gradient | Local | No | Very fast |\n",
    "\n",
    "**Notes:**\n",
    "- **Local/Global**: All explainers are local, because they only explain how a specific input affects a specific forecast.\n",
    "- **Additivity Property**: Whether the sum of the feature attributions and baseline predictions result in the final forecast.\n",
    "- **Speed**: \n",
    "  - Very fast: Single gradient computation\n",
    "  - Fast: Multiple gradient computations (Integrated Gradients)\n",
    "  - Medium: Multiple model evaluations\n",
    "  - Slow: Many model evaluations for sampling-based methods\n",
    "  - Very Slow: Exponential complexity in worst case (exact Shapley values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46e57515",
   "metadata": {},
   "source": [
    "#### Integrated Gradients\n",
    "\n",
    "Integrated Gradients computes attributions by integrating gradients along the straight-line path from a chosen baseline input (e.g., black image, zero embedding) to the actual input. The method calculates the path integral, which is approximated using a Riemann sum with typically 20-300 gradient computations. Learn more in the [original paper](https://arxiv.org/pdf/1703.01365).\n",
    "\n",
    "**Advantages**\n",
    "- Theoretically grounded: Satisfies the axioms of sensitivity (features that affect the output get non-zero attribution) and implementation invariance (functionally equivalent networks produce identical attributions)\n",
    "- Has the additivity property\n",
    "\n",
    "**Disadvantages**\n",
    "- Relies on choosing an appropriate baseline that represents \"absence of signal\". By default, we use a input only 0 values.\n",
    "\n",
    "#### Shapley Value Sampling\n",
    "Shapley Value Sampling approximates Shapley values using Monte Carlo sampling of feature permutations. The method randomly samples different orderings of features and computes how much each feature contributes by comparing model predictions when that feature is included versus excluded from the subset. The approach simulates \"missing\" features by drawing random values from the training data distribution. Learn more in the [original paper](https://www.sciencedirect.com/science/article/abs/pii/S0305054808000804).\n",
    "\n",
    "**Advantages**\n",
    "- All subsets of input features are perturbed, so interactions and redundancies between features are taken into account\n",
    "- Uses simple permutation sampling that is easy to understand\n",
    "\n",
    "**Disadvantages**\n",
    "- High computational cost: requires many model evaluations (typically hundreds to thousands) to achieve reasonable approximation accuracy\n",
    "- Very slow due to the high number of model evaluations\n",
    "- Simulates missing features by sampling from marginal distributions, which may create unrealistic data instances when features are correlated\n",
    "\n",
    "#### Input X Gradient\n",
    "Input X Gradient computes feature attribution by simply multiplying each input value by the gradient of the model output with respect to that input. This corresponds to a first-order Taylor approximation of how the output would change if the input were set to zero. This means each time step's input values are multiplied by the gradients to show which historical observations most influence the prediction. Learn more in the [original paper](https://arxiv.org/pdf/1605.01713).\n",
    "\n",
    "**Advantages**\n",
    "- Computational efficiency: it requires only a single pass through the model\n",
    "- No approximations as it uses the gradient of the model\n",
    "\n",
    "**Disadvantages**\n",
    "- No additivity\n",
    "- A bit problematic with the ReLu function, because their gradient can be 0, but it can still carry some information\n",
    "- Functions like tanh or sigmoid can have very low gradients, even though the input is significant, so it's problematic for LSTM and GRU models."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e49ead52",
   "metadata": {},
   "source": [
    "### Explaining models with different loss functions\n",
    "\n",
    "Currently, explanations are supported for models trained with:\n",
    "- Point loss functions (MAE, MSE, RMSE, etc.)\n",
    "- Non-parametric probabilistic losses (IQLoss, MQLoss, etc.)\n",
    "\n",
    "We do not support yet explaining models trained with parametric loss functions, like Normal, Student's T, etc.\n",
    "\n",
    "For more information on the different loss functions supported in *neuralforecast*, read [here](https://nixtlaverse.nixtla.io/neuralforecast/docs/capabilities/objectives.html).\n",
    "\n",
    "#### Explaning a model with a probablistic loss function\n",
    "If you are explaining a model with a non-parametric loss function, then by default, we only explain the median forecast. This is controlled by the `ouputs` parameter. Let's see an example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a3a30ecb",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "\n",
    "# Initialize model\n",
    "models = [\n",
    "    NHITS(\n",
    "        h=12,\n",
    "        input_size=24,\n",
    "        hist_exog_list=[\"y_[lag12]\"],\n",
    "        futr_exog_list=[\"trend\"],\n",
    "        stat_exog_list=['airline1'],\n",
    "        loss=MQLoss(level=[80]),\n",
    "        max_steps=20,\n",
    "        scaler_type=\"robust\",\n",
    "    ),\n",
    "]\n",
    "\n",
    "nf = NeuralForecast(\n",
    "    models=models, \n",
    "    freq=\"ME\", \n",
    ")\n",
    "\n",
    "# Fit model\n",
    "nf.fit(\n",
    "    df=Y_train_df,\n",
    "    static_df=AirPassengersStatic\n",
    ")\n",
    "\n",
    "# Get predictions and attributions\n",
    "preds_df, explanations = nf.explain(\n",
    "    outputs=[0], # Explain only the median forecast\n",
    "    static_df=AirPassengersStatic,\n",
    "    futr_df=futr_df, \n",
    "    explainer=\"IntegratedGradients\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5a4a9e4",
   "metadata": {},
   "source": [
    "Above, by specifying `outputs=[0]`, which is the default value, we only explain the median forecast. However, we can explain up to three ouputs:\n",
    "1. Median forecast\n",
    "2. Lower bound\n",
    "3. Upper bound\n",
    "\n",
    "As such, to explain all outputs, we must set `ouputs=[0,1,2]`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0982be89",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "\n",
    "preds_df, explanations = nf.explain(\n",
    "    outputs=[0, 1, 2], # Explain all outputs\n",
    "    static_df=AirPassengersStatic,\n",
    "    futr_df=futr_df, \n",
    "    explainer=\"IntegratedGradients\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cf8b638e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of insample: torch.Size([2, 12, 1, 3, 24, 2])\n",
      "Shape of futr_exog: torch.Size([2, 12, 1, 3, 36, 1])\n",
      "Shape of hist_exog: torch.Size([2, 12, 1, 3, 24, 1])\n",
      "Shape of stat_exog: torch.Size([2, 12, 1, 3, 1])\n",
      "Shape of baseline_predictions: torch.Size([2, 12, 1, 3])\n"
     ]
    }
   ],
   "source": [
    "for key in list(explanations[\"NHITS\"].keys()):\n",
    "    print(f\"Shape of {key}: {explanations['NHITS'][key].shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8f52482",
   "metadata": {},
   "source": [
    "As you can see, the fourth dimension, which represents the number of ouputs, is now equal to 3, because we are explaining the median, the lower bound and upper bound."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2e8c524",
   "metadata": {},
   "source": [
    "### Explaining models with a scaler (`local_scaler_type`)\n",
    "\n",
    "If you specify a `local_scaler_type` in your `NeuralForecast` object, note that the attribution scores will be scaled. This is because the data is scaled before the training process. The relative importance is still relevant, but note that additivity will not hold.\n",
    "\n",
    "If additivtiy is important, then you must use `scaler_type` when initializing the model, as we do in this tutorial. This scales each window of data during training, so we can easily inverse transform the attribution scores.\n",
    "\n",
    "Again, no matter which approach you choose, the relative attribution scores are still valid and comparable. It's only additivity that is impacted. If you specify a `local_scaler_type`, then a warning is issued about additivity.\n",
    "\n",
    "### Explaining recurrent models\n",
    "\n",
    "You can explain recurrent models (LSTM, GRU). Just note that if you set `recurrent=True`, then the Integrated Gradients explainer is not supported. If `recurrent=False`, you can use any explainer."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdd064ac",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "M. Sundararajan, A. Taly, and Q. Yan, “Axiomatic Attribution for Deep Networks.” Available: https://arxiv.org/pdf/1703.01365\n",
    "\n",
    "S. Lundberg, P. Allen, and S.-I. Lee, “A Unified Approach to Interpreting Model Predictions,” Nov. 2017. Available: https://arxiv.org/pdf/1705.07874\n",
    "\n",
    "J. Castro, D. Gómez, and J. Tejada, “Polynomial calculation of the Shapley value based on sampling,” Computers & Operations Research, vol. 36, no. 5, pp. 1726–1730, May 2009, doi: https://doi.org/10.1016/j.cor.2008.04.004.\n",
    "\n",
    "A. Shrikumar, P. Greenside, A. Shcherbina, and A. Kundaje, “Not Just a Black Box: Learning Important Features Through Propagating Activation Differences,” arXiv:1605.01713 [cs], Apr. 2017, Available: https://arxiv.org/abs/1605.01713"
   ]
  }
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